What is Synthetic ?

Plastic raincoats, for example, are waterproof, and stretchy Lycra keeps its original shape.

The benefits of using synthetic data include reducing constraints when using sensitive or regulated data, tailoring the data needs to certain conditions that cannot be obtained with authentic data and generating datasets for software testing and quality assurance purposes for DevOps teams.

What does it mean if something is synthetic?

The synthetic method starts like induction from the observed facts and the inferred theory (but it can also start like deduction from a set of assumptions).

Synthetic polymers are derived from petroleum oil, and made by scientists and engineers. Examples of synthetic polymers include nylon, polyethylene, polyester, Teflon, and epoxy.

Synthetic data is information that's artificially manufactured rather than generated by real-world events. Synthetic data is created algorithmically, and it is used as a stand-in for test datasets of production or operational data, to validate mathematical models and, increasingly, to train machine learning models.

The benefits of using synthetic data include reducing constraints when using sensitive or regulated data, tailoring the data needs to certain conditions that cannot be obtained with authentic data and generating datasets for software testing and quality assurance purposes for DevOps teams.

Financial services and healthcare are two industries that benefit from synthetic data techniques. The techniques can be used to manufacture data with similar attributes to actual sensitive or regulated data. This enables data professionals to use and share data more freely.

Synthetic data is gaining traction within the machine learning domain. Machine learning algorithms are trained using an immense amount of data, and collecting the necessary amount of labeled training data can be cost prohibitive.